Liu, Qi

A Survey on Large Language Models for Recommendation

Wu, L., Zheng, Z., Qiu, Z., Wang, H., Gu, H., Shen, T., Qin, C., Zhu, C., Zhu, H., Liu, Q., et al. (2023) · arXiv

Recommender Systems and TechniquesInformation Retrieval and Search Behavior

Cited by 515

A survey on large language models for recommendation

Wu, L., Zheng, Z., Qiu, Z., Wang, H., Gu, H., Shen, T., Qin, C., Zhu, C., Zhu, H., Liu, Q., et al. (2024) · World Wide Web

Recommender Systems and TechniquesInformation Retrieval and Search Behavior

Cited by 515

Adaptive Quantitative Trading: An Imitative Deep Reinforcement Learning Approach

Liu, Y., Liu, Q., Zhao, H., Pan, Z., & Liu, C. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence

Stock Market Forecasting MethodsFinancial Markets and Investment Strategies

Cited by 141

Cross-scale multi-instance learning for pathological image diagnosis

Deng, R., Cui, C., Remedios, L. W., Bao, S., Womick, R. M., Chiron, S., Li, J., Roland, J. T., Lau, K. S., Liu, Q., et al. (2024) · Medical Image Analysis

AI in cancer detectionRadiomics and Machine Learning in Medical Imaging

Cited by 95

Towards Automatic Discovering of Deep Hybrid Network Architecture for Sequential Recommendation

Cheng, M., Liu, Z., Liu, Q., Ge, S., & Chen, E. (2022) · Proceedings of the ACM Web Conference 2022

Recommender Systems and TechniquesInformation Retrieval and Search Behavior

Cited by 26

FinPT: Financial Risk Prediction with Profile Tuning on Pretrained Foundation Models

Yin, Y., Yang, Y., Yang, J., & Liu, Q. (2023) · arXiv

Financial Distress and Bankruptcy PredictionCustomer churn and segmentation

Cited by 4

Increasing depth, distribution distillation, and model soup: erasing backdoor triggers for deep neural networks

Zhang, Y., Zhang, T., Liu, Q., Sun, G., & Wu, H. (2022) · Journal of Electronic Imaging

Adversarial Robustness in Machine LearningDigital Media Forensic Detection

Cited by 2

Vibe-Eval: A hard evaluation suite for measuring progress of multimodal language models

Padlewski, P., Bain, M., Henderson, M., Zhu, Z., Relan, N., Pham, H., Ong, D., Aleksiev, K., Ormazabal, A., Phua, S., et al. (2024) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 1